Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score
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Abstract
Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.
Methods
Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).
Results
983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS ( S pO2, O besity, A ge, R espiratory rate, S troke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).
Conclusion
The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
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SciScore for 10.1101/2020.10.19.20215426: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Development and external validation of the clinical risk score: The large external cohort comprised a randomly selected sub-population of the ISARIC 4C derivation population (N=20,000 provided; 14,231 with complete data for scoring). Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All statistical analyses including risk modelling calculations were performed using STATA, version 16 (Stata Corp., Texas, USA). STATAsuggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. …
SciScore for 10.1101/2020.10.19.20215426: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Development and external validation of the clinical risk score: The large external cohort comprised a randomly selected sub-population of the ISARIC 4C derivation population (N=20,000 provided; 14,231 with complete data for scoring). Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All statistical analyses including risk modelling calculations were performed using STATA, version 16 (Stata Corp., Texas, USA). STATAsuggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Other limitations in the study include the occurrence of missing information despite prospective data collection. The use of multiple imputation to estimate missing values for multivariate regression and the availability of nearly 85% of observations for constructing the risk stratification rule helped to mitigate against underestimating their role. The modest sample size of our derivation cohort was dictated by the incident caseload during the pandemic. However, selective sampling of the pandemic timeline was avoided by including all COVID-19 cases from the initial rise to the subsequent decline in new case numbers over the 11-week study period. Finally, reduced score calibration at the high-risk end suggests that SOARS may overestimate the probability of death in the highest risk cases. However, the principal objective of this score was to enhance frontline decision-making in patients with a low predicted risk of mortality at a time when demand for in-patient resources are likely to be high. In summary, prognostication using the SOARS score can be undertaken concomitantly with SARS-CoV-2 diagnostic testing to inform clinical triaging, including decisions about the placement of the patient for ongoing care. Analysis of the ISARIC validation cohort in this study showed that between 16.6% and 29.8% (those scoring up to SOARS 1 or 2 respectively) could potentially have avoided admission provided a safe alternative to hospitalization was in place. Prospective studies of SOARS im...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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